1005: People Skills for Analytical Thinkers, with Bestselling Author Gilbert Eijkelenboom

Speaker A: Today's guest was a professional poker player who read his opponents entirely through data, never their faces, and is here to explain why the people side of data science and other technical disciplines might actually matter more than the math. Welcome to episode 1005 of the Super Data Science podcast. I'm your host, Jon Krohn. Today's guest, Hilbert Eichelinbaum, is an absolute delight who emanates practical guidance for all of us technical folks who would like to make a bigger impact in our organizations and in our world of through more persuasive, more influential people skills. Gilbert wrote the bestselling book People Skills for Analytical Thinkers, and he runs a firm called MindSpeaking that has trained over 15,000 people. Folks love his invaluable content. He has over 200,000 followers. I hope you'll love this episode, too. This episode of Super Data Science is made possible by Anthropic, Excel, Data, Gurobi, and Notion Gilbert. Or if I try to get the pronunciation right, Hilbert. Welcome to the Super Data Science podcast. It's great to have you on the show. Where are you calling in from?

Speaker B: Thanks, John. Thanks for having me. I'm calling from Amsterdam, the Netherlands. So it's, um, the city with more bikes than people. That's kind of interesting.

Speaker A: Yeah. And as you and I were talking about before we started recording one of my favorite cities in the world in one of my favorite countries in the world, I think you're super lucky to be there all the time. All right, let's jump into you. You're a former data analyst, and you claim to have once struggled with communication yourself, but you turned that frustration into a number one bestselling book called People Skills for Analytical Thinkers, which, of course, we'll have in the show, Notes for People. And I think your story, your narrative there, at least, if it is true that you did once struggle with communication, then you prove that kind of interpersonal skills are something, uh, you know, is something that can be learned. It's not necessarily something innate. What do you think about that?

Speaker B: Yeah, I agree. I mean, I've experienced it myself, as you mentioned, where I started my career in data analytics, and I was so focused on doing the analysis or building the dashboard that when it came down to presenting it, then at some point I was so surprised because, for example, I was presenting to this marketing manager, and so his name was Giovanni, and he asked me to come up with some insights. So I did a research for analysis for a few weeks. Then I presented it, and he said, like Gilbert, this isn't helpful. Even Though I thought my analysis is solid, right. I have some recommendations, but I clearly focus way too much on the technical side, not on the communication, um, and how to make it relevant for, for him. And it was a good lesson. So after learning, spending some time with my manager, with other people, getting feedback that was clear. It was the insight, no matter how good your analysis or no matter how good your model, if you're a data scientist, only if people use it, it's valuable. Right. And it's a painful truth because many data professionals, including myself in the beginning, focus so much on doing good work, but misunderstanding that good work is not just doing the work, but also making sure that it's useful. So, yeah, no, it's absolutely possible to learn communication, even if you feel like I'm an introvert or I struggle with these things. It's just that many data people, many analytical type of people, they think so much that it's in the way of just being present and having a conversation and listening and feeling like, hey, what's important for the other person? And that's what really matters.

Speaker A: Well, it does seem like your, uh, presentation skills are now at least super refined. You're so good on this episode already. Um, something that we dug up in our research on you is that in addition to having your past as a data analyst, is it correct that you were also a professional poker player?

Speaker B: Yeah, that's right. That's right. It was a bit earlier in my student time. Yeah.

Speaker A: That's kind of interesting because I feel like being a really good poker player, you've got to be able to read even the most subtle interpersonal cues about other people. So it's interesting that you had honed that somehow, um, without feeling like you were really great at presenting your data analytics findings to, say, Giovanni.

Speaker B: Yeah, I see what you mean. And the reason is kind of simple because I played mainly online. So poker, uh, is a game of math, especially online, where I relied on not so much cues from behavioral cues from people, but more on what does the data say. Because what I did as a professional poker player, I paid a lot of money to get all the hand histories in the database, meaning they'll send me all the, the complete history of the past month of all the cards that were played, and that told me exactly how people were playing. So if I got on a table that I. Where I didn't knew anybody, for example, with you, I could see like, hey, this guy, he's pretty aggressive. So he's always batting, but he doesn't always have a great Great.

Speaker A: Sure, sure. Or.

Speaker B: Or this guy never does anything, but now he's raising a lot now. So now I need to be careful. So I just could click on you, on your icon, and then hundreds of data points appeared that told me what to do or at least gave me insight in what type of player you were.

Speaker A: No way.

Speaker B: And based on that, I could make decisions. And of course, it's still. You still need to make that decision, but it's way more informed. And that showed me, like, I can analyze a lot of things. But of course, the problem is, outside of the poker table, I didn't have the statistics. If you meet someone, if you are engaging in small talk or meaningful conversation, you don't have that information about people. Like, what's best to talk about, what not to talk about, how to engage people, what type of question you got. You need to figure that out. It's very messy, and that's hard for an analytical brain like me that wants structure, that want to be prepared. And that's what I see with many data people as well. They want to know the rules, but the social rules, it's very messy.

Speaker A: It's wild to me that you're able to buy those data. Anybody could have bought the data on historically how people were playing in that platform.

Speaker B: Yeah, anybody could do that. I'm not sure how it is now. I'm very much out of that world now, but in 2008, um, everybody could do that, but it's just very expensive, so it's only valuable or it's only. There's only a return if you. If you make money, if you play

Speaker A: a lot and you use the data well. Is that how you got into data analytics or did data analytics kind of come ahead of the poker?

Speaker B: No, data analytics came. Came later. So I started my career as a data analyst and thinking, hey, it's so interesting this field is coming up. It was 2014, it was getting bigger and bigger. I found it very interesting to use my analytical skills to. To find insights. But also then I was kind of disappointed, like, hey, that's not everything you need. Right. You also need the presentation skills and stakeholder management and understanding what they need and shaping your story accordingly. Um, so it was a disappointment initially, but then I got so obsessed with communication and understanding people, the people side of things, that I also started teaching others. First colleagues, then external people. Until 2020, it was a middle of COVID I founded MindSpeaking to the company that is present today. And now we have a team of 10 people around the world in the U.S. um, working with companies and their data teams to help them on that people side, making sure all the valuable work that they're doing is actually making an impact and not leading to frustration and wasted work.

Speaker A: And the scale of what you're doing seems to be pretty impressive. You can update me or correct me on these data points, but it seems like uh, from the research that we had, over 15,000 people have been trained m by that agency Mindspeaking that I guess you just founded in 2020 during the pandemic. And over 200,000 people are being impacted via your various social media and marketing platforms like newsletters, that kind of thing.

Speaker B: Yeah, that's right.

Speaker A: Yeah.

Speaker B: Uh, it started on LinkedIn. I started writing there. I felt so anxious posting stuff in the beginning, but I also felt, hey, this is important. This is a message I really want to share. This is something I struggled with personally. So I started talking about it and it was not very refined. It was, I don't know, just sharing stories about what I struggled with and lessons I'd learned. And slowly that started growing. And last year I also started posting on YouTube, which is a new thing that's also a fun new communication challenge for me as well. It's how to make things very simple, simple to understand and helping people with communication and making their work valuable.

Speaker A: Yeah, we recently had, in episode 987 we had a really well known creator, uh, in the AI space called Linda Haviv and she, she has hundreds of thousands of followers. She's particularly big on Instagram which is interesting. I think it's like a different, you know, that kind of Instagram TikTok like vertically oriented. I think it's something that, you know us at the podcast me individually like I'm trying to have better and better content like that all the time. Um, but she's, she's mastered it and she's been doing it for a long time. But the point is that in that episode it, it comes very close to what you were just saying, where for people who have the impulse, even just a little bit, it can be worth it to start publishing publicly on whatever. It could be, you know, an open source project. You know, it doesn't have to be like a narrative, it doesn't have to be on the news. I think a lot of people already do that. But if you, if you write some code or you use code shed tools to help you write some code. But uh, you really understand it and you can explain it or you can even talk about online how you know the latest Things in Claude code or whatever, um, how those things are working for you. By creating that kind of, um, by creating that kind of content, publishing it, I think it helps you understand what you're doing better. And while it can be scary to do it at first, when you get in a regular cadence, if you commit, you're going to say okay, once a month on this day of the month, I'm always going to publish. So you know, just 12 times a year. It's not a crazy amount, but that's going to be my initial schedule. Try that for a year, see how it goes. I think pretty much everyone will be surprised at how, you know your nerve, it gets easier on your nerves and you know, there's no guarantees, but there's a good chance if you listen to feedback, you show it to some colleagues or whatever, you'll probably get feedback, you'll be able to improve. And after a year of doing that on some kind of schedule, you'll probably get some kind of results in terms of audience as well.

Speaker B: Do you remember doing the very first

Speaker A: podcast episode of this, of the Super Data Science podcast? Yeah, um, yeah I do for sure. So regular listeners will know or longtime listeners will know that I wasn't always the host of this show. So from episode one through, which was in fall 2016, September 2016 to episode 431, uh, the host was Kirill Aramenko who's an amazing content creator, uh, specifically like technical courses on AI or data analytics. He sold millions and millions and millions of courses, uh, uh, copies of courses on udemy. And yeah, he was the original host, he invited me to take over and so we co hosted one episode together at the end of 2020 and then all of a sudden January 1, 2021, I was just hosting all on my own. And so, so I absolutely remember because it was a scary experience, uh, because usually when you start creating content in some way you, you know, you, you can start with nobody watching and you can kind of iterate. And I had been since about 2016, well, going back a little bit further, I think since about 2014. In 2014 I started writing on LinkedIn. Um, and then 2016, 2017, I started giving public talks on deep learning, like the mathematics of deep learning and at that time tensorflow and then Keras and how people can be using those uh, open source libraries to be uh, building artificial neural networks. Uh, so I had had this kind of iterative experience on creating content and it had even led to already having a best selling book. But I, I had didn't I hadn't been podcasting to thousands of people before, and so it was a pretty. Yeah, you're, you know, I was being trusted by Kiril to not mess it up. And you, like, I try to, like, he created this 20 page document on how I should do it as part of the handover. And so I, like, studied that meticulously. Um, but yeah, I don't know, I don't know why, what your, uh, hope was in asking that question, but I probably just talked way too long.

Speaker B: Yeah, My hope was to understand a bit more about your experience and potentially also how you get into that first episode by yourself. Right. Because I can imagine many of the listeners can relate to that. Not necessarily having a podcast, but presenting their model or their data work for the first time to a bigger group. Um, I think it's important to understand that it's always, it's always very scary. Right. But committing to, you know, posting on social media or presenting your work to a bigger group, those things are also what create or propel your career forward so much. You get feedback, you get more confidence as well, because confidence, you can read 500 books about how to present better, how to be more confident socially, but until you actually do it and mess up, and that's when the confidence shows up. I remember I really struggled with saying no and setting boundaries because my parents were often fighting. They're still together, so there's also an evidence that disagreement and some conflict does not always lead to separation. But in that moment, as a child, I was trying to be the mediator. I tried to solve the conflict for them, which was not very healthy. But I also learned that, you know, I want to avoid conflict at all costs. And also as a data analyst, when people ask me for a dashboard, I just said, okay, yes, I'll create it. Uh, even though I didn't ask many questions, I just deliver something and then they didn't use it. But I would be so much more valuable if I would ask questions about why they need it and what's the goal and what would make this a success. But I didn't do that, um, partially because I didn't have the skills. I didn't know that was important, but also partially because I was kind of afraid. So I started doing all these little exercises and games where in the train there's this silent part where you're not supposed to talk. So when I was sitting there and people would speak or listen to music, I made it a rule that I had to go to them and tell them, hey, do you see that? Do you know this is a silent part of the train? And I hated it so much that I made it real for myself. But I was also committed.

Speaker A: Yeah.

Speaker B: Um, so a few times I talked myself out of it, not being stuck. Committed after all. But then I said, like, okay, okay, I'm gonna stand up. And then all these thoughts going through my mind thinking, okay, they're probably gonna get angry or they're gonna hate me, or they're just gonna just keep talking. Um, but usually they said, like, oh, I didn't notice. Oh, I. I'll move or I'll be a bit more silent. And that also proved to me, to my system, like, hey, it's not that bad, you know, um, that helped me to become a bit more confident and speak up. And even though I'm more introverted, I need my recharging space. I can also say what my needs are and what's important to me.

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Speaker B: Exactly. I can bring that to the story. Yeah. Um.

Speaker A: Uh, no, that's a great. That's a great little trick. And I like. So something that you talked about there. Was it being a rule and. Yes. You know, sometimes maybe for whatever reason it was a particularly scary person or whatever that you don't go over or maybe, you know, maybe you've already been having a really bad day and, um, you know, there's. You're just like, you know what I can't handle. I can't. I can't handle doing that today. And that's okay. But I think the key thing is, like, trying to commit and most of the time doing it, like, feeling like you've made that commitment. And I think it's the same thing with content creation. In order to be doing it successfully. If you want to be, if you want to be successful at creating content, the reality is you're very rarely going to feel inspired to do it. You know, you, if you wait around for like, oh, that's an amazing idea, or oh my goodness, the world needs to know this. If you wait for those inspiring moments, you'll stop creating content or it will become very slow. You need to say, you need to commit. That's the key thing. You say, like, uh, you know, on the, on the fourth or on the, the second Tuesday of every month, I am going to make a LinkedIn post. And you know, even if that means sometimes you just say, hey guys, um, I just had a kid. And so today's, you know, I had a kid yesterday, so today's post, there isn't going to be anything but I committed to writing something. And so this is it.

Speaker B: Exactly, exactly. And sometimes you don't really feel inspired, but you sit down and start writing or start creating, depending on what type of content is. And then slowly you get more ideas. But yeah, I know there's many people. So let's say I want to build a business, but I don't have the idea yet. But that's not how it works. In my experience, you never get like, you wake up and get the idea and then do it. It's more like if some vague idea, you working on that, talking with people and then it develops. I've never met any entrepreneur that said like, wake up and then I got it for sure.

Speaker A: Yeah. Once I actually heard in Union Square, which is a, um, it's a part of Manhattan in New York that sometimes has, uh, quite a few homeless people around it. And I remember once I was. My gym CrossFit Union Square was in Union Square and I was walking to some meeting or something or maybe walking to the, to the bikes, the shared bike scheme in New York. And I overheard one homeless person say to another, like, uh, you know, if only I just had that one big idea, everything would be different. You know, I'd be so successful and, you know, I wouldn't be homeless. And I don't know, I mean, there's obviously, you know, that's homelessness and what leads to that. It's a very complex issue. I don't mean to try to like, um, trivialize that situation. I think, you know, it's a, it's a, there's, there's lots of circumstances where that can't be avoided. Um, but I just, that really stuck with me because it's basically exactly what you're saying. It's like it's this. I think it's a common misconception that it's about having the idea, it's about kind of, you know, deliberately like uh, it might even be helpful to not have any bias around some ideas. Just speaking to, you know, if you already are a data analyst or a data scientist or a software engineer or whatever, to just take meetings with people where you don't have any idea of what the problem to solve is, you don't try to tell them. You just say what are pain points that you have? Like tell me about your day and you know, where, where, where do you encounter issues where maybe there could be some kind of software solution to it, something like that.

Speaker B: 100%. And if you understand their, their business problems better, you become so much more valuable as a data scientist or data analyst or any type of data role. And yeah, I think there's a lot of parallels to what you're saying. And also the data work where a lot of data people, they, they want to polish their work until it's perfect, until we have 100% accuracy, uh, or approaching that. But in business things move so fast that it's often way more valuable to create something like a little draft or, and then show, hey, this is where this is going. Um, what do you think? What are your thoughts? And then get their input. Because this does two things. One is it helps you shape the direction M. It helps you make it more valuable. And also once it's their idea or at least partially, they will also be more invested in the idea and the end product that you're solving. So that's also how you get their buy in. So I've, I found that doing that is, is makes a massive difference for sure.

Speaker A: And it seems like a big part of the mind speaker mindset that you through your mindspeaking agency as well as through your book people skills for analytical thinkers. This mindspeaker mindset is a lot about not being afraid to tell the truth and speak honestly about your opinion, but it's also about being able to identify opportunities where you can make an impact. So it's about not taking direction top down about uh, like oh, this is my job description or this is the task that was asked of me. It's not about executing the task as accurately as possible to the request. It's about saying actually is this request going to deliver the outcome that the stakeholder wants? Like I, I I know that you've told me, vice president of my business, that you want this kind of dashboard, but actually, you know what? We don't really have the data that you need for that dashboard to be effective. Or, you know, I can, I can make that dashboard for you, but that particular information, it's not going to help you be more profitable or it's not going to help you increase revenue for these reasons, 100%.

Speaker B: And that's how you become so much more valuable, because just taking orders from people that also don't know exactly what they need, they ask for something, but that's often not what. What, what, what's needed. Right. And our job in data is also to understand that part. And I remember at the beginning of my career I asked for feedback from, from one of my clients, I worked at Philips back then, and Mark was my manager. And, and overall the feedback was, was pretty good. He said, like, it's very valuable. I like working with you, all those things. And I also asked him, like, how can I improve? And he said exactly this. Like it's, it's the truth. We, we also don't know. He was like 50 plus, he's very senior business person. But he said, like, the truth is we, uh, don't know. So we want you to tell us which direction we need to take. So we want you to become more proactive in sharing that and more confident in doing so instead of waiting more and then still be kind of valuable providing what we say we need. Um, so that was a good lesson.

Speaker A: Nice. Great anecdote. I love how you, uh, you actually use these people's names. We had Mark Giovanni. I feel like most people kind of make it much more anonymous, but I like it. It makes it kind of easier to know exactly what story you're telling about, um, a statistic from your book that blew my mind. But I'd also love to just have you explain to me what the definition means a bit more is you cite research showing that only 15% of people are self aware. So what does that mean? What's the definition of being self aware? Because I'm sure it's not something that's binary. It makes it kind of seem easy. Okay, 15% of people are self. Aware. 85% are not. Like, I'm sure it's more of a gradient and there's degrees of self awareness, but yeah, so what does it mean to be self? Aware? And presumably, if that statistic is true, and if listeners to this podcast are, uh, representative sample of the population, then 85% of listeners. So the vast majority right now are not technically self aware by whatever that definition means. So I'm probably not self aware, it turns out as well. So what does it mean to be self aware in that definition? And how can you foster more of it?

Speaker B: Yeah, uh, so I believe it has been like it was a few years since I wrote the book, but I need to think about the exact research, um, they used. But I believe their definition was that the perception of yourself, your behavior and your, your thoughts is, is overlapping a lot with how other people see you. So if I see myself as a very confident presenter or, or maybe the opposite. So if I see myself as a very shy person, but other people tell me, hey, um, you talk to everyone, uh, you have your opinions ready, you don't wait to talk all those things, then it's, it's not really matching. So then I might not be so self aware. But if I'm more self aware, I understand my own behavior. I can have that meta conversation, not just being in the conversation, but also saying like, okay, here I'm, I'm rambling a bit or let me pause there because I want to make it more concise. So in the moment, being able to take the helicopter view and see your own behavior in action. And it's so important because everyone is different, right? Everyone is different. I. And the more I learn about myself, and the more you learn about yourself, the more effective you can be in communication and relationships with other people, even your personal life. I mean, for people that have a spouse or partner, if you don't know yourself really well, it's getting difficult because they see certain behavior more accurately sometimes than others. Then you also ask, how can you become more self aware? In my experience, the best ways to become more self aware is one journaling, because it forces you to articulate your thoughts and feelings in the moment and you can look back at it. So I have my journal from years ago and I see my journaling from 2020 and what I struggled with back then. And I look at that, I'm like, hey, okay, I think I've grown a little bit because the things I was worried about back then don't concern me as much. So journaling is massive. And then second is meditation helped me a lot because it forces me to stop. There's a lot of thoughts in my head always. I'm a big overthinker. And thinking is useful in some situations. But often it's also not useful if we're having this conversation and I'm thinking like how do I come across will the audience like this? Is he judging me? And what do I have for dinner tonight? Uh, it's not, it's not useful. Right. I'm getting out of the moment. I'm not having this conversation anymore. So meditation and journaling helped me a lot. Sports, exercise, physical, because it gets me into my body more instead of only thinking up by my head. And you can see it as well on the street, people having their phones being fully disconnected from others. And if you just take, if you're in the queue of the supermarket, everyone pulls out their phone, right. Or if you go to the bathroom, pull out your phone. I, I do it as well, but I try not to do it as often. And if I don't do that, it creates a space to let the thoughts come and also let them disappear. And it creates so much clarity and self awareness.

Speaker A: Really great tips there for the journaling. I feel like that's something, I don't know, maybe you have some specific tips on how people could be journaling more effectively or how they could get, be getting started on that. But especially for the meditation part. Is there a particular like program or technique or app or something that you recommend?

Speaker B: Many people like Headspace, I love that space. Friendly. Yeah, I uh, I have a calm subscription. I mean they're all very similar. There's also Inside Timer which is kind of YouTube for meditation, uh, which is free. So it's very uh, easy to uh, to start with. And I would say the program doesn't matter as much. Uh, just starting somewhere. Sam Harris. For many people who like more evidence based or are more analytical, you can check out the app of Sam Harris. I think it's called Wake up not sure.

Speaker A: Yeah, Waking Up I think is the app, which is also the name of a great book by him that I love. If people kind of want to, if you want a book to kind of accompany your self awareness journey, then the Waking up book is pretty cool that it has, it kind of, it uses neuroscience and just anecdotes that you can experience yourself to help you understand your mind better. And so for example, something that people find really trippy is there's, there's something called the corpus callosum, which is this huge track of nerve fibers that connects the right hemisphere and the left hemisphere of your brain. And some people due to really severe seizures, as a last resort, surgeons cut that corpus callosum, um, connecting your two hemispheres. And what happens then is you end up having two separate conscious people living in one body. And so if you, if you do things like, if you put like a piece of paper in between their eyes, you can present information to uh, you know, these two different people and you can get different responses. So only one will have access to the mouth. Um, and depending on whether you're right handed or left handed, um, it would typically be one, one specific hemisphere. But so only one can speak, but you can have the, the other hemisphere can communicate say by pointing to images or responses on a table. Um, and so you can show that there's these two completely separate people living in the same body. Um, and then it kind of begs the question, well, uh, could, could you actually have even more than two, you know. You know and yeah, it's kind of an interesting, yeah, interesting thought experiment.

Speaker B: 100%. And learning about neuroscience is fascinating to me and especially for people who are more analytical who might see communication and behavior as, I don't know, soft skills or secondary. I think behavioral science or uh, neuroscience is a great way to also learn about behavior but a more, maybe scientific approach or more learning about the brain and how we make decisions because this is key, right? Because all the data work. What is a model or dashboard or analysis or many AI products, they help people make better decisions. So the more we learn about how people make decisions, how business stakeholders make decisions, the more effective we can be and the more impact we can have in our career. There's so many things to learn about um, the human brain and it's fascinating.

Speaker A: Machine learning predicts and Genai creates, but neither is built for complex constrained decisions. That's where mathematical optimization comes in, giving you explainable, trustworthy decisions you can act on with confidence. Gurobi is the fastest, most reliable solver organizations rely on for their high, high stakes decisions. Want to see it in action? Join the 2026 Gurobi Decision Intelligence Summit September 22nd and 23rd in Las Vegas for training expert insights and Gen AI enabled accessibility. Discover why 70% of the world's leading enterprises trust Gurobi and start achieving optical outcomes yourself. Head to superdatascience.comGurobi for the conference details. That's superdatascience.comGrobi MHM and so presumably as technical people like a lot of our listeners are, uh, a lot of us are dealing with uh, graphs and data. And you've previously mentioned that a graph is not just a story, you also need a uh, narrative. And so something that's interesting there is that then having a persuasive narrative on top of data can of course you know it can, it can manipulate or it can be aligned to interest. In fact, there's the famous quote from Mark Twain, There are three kinds of lies, lies, damned lies, and statistics. And uh, so how do you, yeah, beyond choosing the right chart or the right data or the right metric, um, what's the deeper craft there of data storytelling that our listeners should take home with them?

Speaker B: The main struggle or the biggest mistake that many data professionals, uh, make is sharing too much complexity and sharing too many details. So the main thing, the narratives you do, their storytelling is taking away the complexity. So that's the start, right? So I would say that's the baseline and it would solve, I would say, not 80, 90% of the, of the problems because many people also know it conceptually, uh, put less information on slides. But then I walk into big corporations and look, work with the data teams and I see slides and it doesn't matter which, which kind of company or how big they are, um, a lot of information on slides. And I know stakeholders will, will check out, they will lose it because they don't, are not the data experts. They have not done that analysis. So they're very fresh and they check out and pull up their phone and check some emails. Um, so now to a question. How do you solve that? So the main, so the easiest framework I know is the end but therefore framework. So many data professionals, they present their data or their model in and, and, and form. What I mean with that, it's, it's like here's some data, here's some more insights, and here's our methodology and here's how we analyze it and here's the decisions we have made along the way. But people are not interested in that methodology. They want to know what's the insight, what should we do next and what's the potential impact. So instead of presenting end and end, a bit like a toddler telling a story, so how was your day? Yeah, first this happened and then that happened and then it's kind of draining. Right. If you're on the other side. So that's important to, uh, to realize. So instead of, and, and, and we need some tension, we need a twist in the story. That's what every good movie has as well. But we don't need to keep it simple. So instead of saying, hey, here's data, here's data, here's data, we can say over the last two years revenue has been quite stable. And um, and that's why sales seems to be performing well. But in the last quarter we found that revenue went down and we analyzed it and we found that we have a lot of new customers, but the returning customers are actually declining. Therefore, recommendation. We need to focus on getting, um, our existing customers to make them return and maybe do a specific recommendation on that, if you have it. And many data people have this misconception, like it's my, not my job to drive decisions to do a recommendation or move people to the next action. But actually that's where you become really valuable. As we discussed that feedback that I got, that's an, uh, important piece of the puzzle that you should not miss. So, and, but therefore, I like that

Speaker A: I've never heard of that before, but it makes a huge amount of sense. Thank you for sharing that with us. You mentioned there how, uh, toddlers can have that and, and, and, and how that can become tedious. You already mentioned earlier in the episode and in your book, it might have been the same personal example, but you share how early family dynamics shaped your tendency to avoid confrontation. And that could have been the thing that you were talking about earlier in the episode with your parents, for data professionals or our listeners who come into the workplace with these algorithms that we learned as children. And then we come into the workplace and it can, it can be detrimental to our performance in the workplace. Uh, you know, you mentioned one already there, where avoiding confrontation can be an issue in the workplace because there can be situations where it's essential. In fact, that's kind of, It's. You're probably not going to be very valuable in your business if you're always just assuming things are correct and going forward with, um, with other people's assumptions and executing in that way, you know, critical thought and pushing back, not to be annoying, but to, uh, you know, check assumptions and fact check and make sure that people have examined a situation from more angles. Yeah, obviously. And I'm sure there's other kinds of early childhood dynamics that can lead to issues in the workplace as well. Yeah. Do you have any, do you have any guidance for us on how we can identify and interrupt a pattern, an algorithm that we learned as children that isn't helpful for us as adults in the workplace. And I suspect that that will be useful not just in the workplace, but outside of it as adults as well?

Speaker B: Yeah, 100%. Yeah. So if we take a step back, like how these algorithms are formed, these personal algorithms. Right. We're not talking about data algorithms here, but the algorithms in our brain. Because when we're young and we touch an oven, it's hot, and we burn ourselves and we Learn, okay, next time if I see an oven, then don't touch it. And this is a simple example how we learn, how we learn behavior and what we're supposed to do and what we're not supposed to do. So maybe one time you had a great idea as a child and you ran to your dad and said, hey, look at this, uh, I can do, uh, I can, I can bike with my, with one hand up or whatever. And at that point your dad was busy or didn't pay attention or said something critical, then you might learn, okay, if I speak up or if I have a great idea, I, maybe I should be cautious, maybe not speaking up. And I see that with as many people who struggle in social situations, they have a lot of these beliefs inside that they're not supposed to speak up or that they're, um, annoying to other people if they talk too much. That's one that I had as well, and I sometimes still have. Um, so how we become more self aware is the steps that we talked about and getting a lot of feedback from others. So not just say, hey, do you have any feedback? Because then people say, yeah, it was great, um, great presentation. But actually force them to also share something that we might not want to hear but is very valuable. One that I like is what should I keep doing, what should I stop doing and what should I start doing? Because it's very specific and it will tease out very valuable information. So that's how you become more, um, more aware of, of your own personal algorithms. Maybe in situations you present too many details or you're too long winded, or maybe you're a bit too focused on the data, you might be better off in other people's eyes. If you talk a bit, show a bit more about yourself, your personal life, and it doesn't need to be mean, it doesn't need to be super vulnerable. But saying something about your hobbies, something I had to learn, is like, it's actually good even if people don't ask about it specifically. Tell something that you're passionate about. You know, I traveled a lot and I like that. So sometimes I bring it up kind of proactively when the situation is there, even when people don't ask me the precise question, hey, do you like to travel? Um, because I had this belief like, you should answer people's questions and stay within the boundaries. Um, but I've learned that people are there to have fun or to have an interaction, right? It's not just about Q and A in the Precise technical way.

Speaker A: 100% yeah, um, I think, yeah, I try to, as much as possible, when things are going well in business, to try to have the meetings be relaxed and have fun and learn a little bit about, you know, what people have been doing in their personal lives. I, um, think especially since the pandemic, I haven't been regularly. You know, I used pre pandemic, I was always daily in an office. And it kind of makes it easy to know what's going on with people's personal lives because you, you know, you chat with, as you have coffee or as people come into the room or whatever. There's lots more opportunities, you know, grab a beer after work, whatever. But, uh, you know, once when the pandemic happened, I would try to make a point of like, okay, just like a little bit of understanding what's going on with people, making jokes where possible. Um, because I think that, you know, you really, obviously it's, it's essential that the critical work gets done, but I think if you can make people feel at ease, then they're more likely to have great insights. They're more likely to push back on you. Um, and I used to, I used to be really pleased with myself about how, you know, post pandemic, um, I felt like I got a lot of laughs in my data science stand up every morning. And, uh, the girlfriend that I had at the time, she didn't find me very funny. And she said the only reason why people are laughing is because you're their boss.

Speaker B: So that was it for you to hear that.

Speaker A: Yeah, I mean, we're not together anymore. I don't, not because she said that, but, uh, yeah, we just didn't, um, yeah, we, we didn't have the same sense of humor for whatever reason. And I think, I think a lot of, I think a lot of kind of data sciencey people, we do have maybe the same, uh, kind of humor. We're all, you know, a lot of us are the same kind of people that like, uh, the TV show Rick and Morty, which is a show that she did not like.

Speaker B: There you go then. It's hard to, to make it work out.

Speaker A: Exactly.

Speaker B: But you're right, everyone is, everyone is different. Right? And data people have a lot of, a lot of things in common. And I think one of the things that, that they have in common is that they don't like small talk. But also on small talk, I kind of changed my mind because I was always like, it needs to be kind of meaningful if we have a conversation. Why, um, have small talk. But it's not about forcing questions that you're not interested in. It's. It's about finding something that you're interested in and then talk about that or, and. And it doesn't need to be the precise same thing. So if you're interested in rock climbing. I don't know anything about rock climbing, but I like sports in general. So I can ask you questions about the rock climbing, but I can relate to that because it's outside. I love outside. I love outdoor. I love sports. So there's different ways how I can relate to that and still have a meaningful conversation. Even though your hobby might be something completely different. Uh, and I think that's something people forget, that curiosity is not just important to connect with other people, but also in the data field, like, curiosity is super important. And that's also what many data people have. But it would be valuable if data people would be equally curious about the data as well as the people.

Speaker A: Yeah, that's a really good tip. And, um, something that's going to be a little bit risky. So you mentioned really liking sports. So we're about to. At the time of recording next week, the World cup is about to start. But by the time your episode comes out, it's going to be June 30, so a lot of the group stage will have played out. So do you support the Dutch national football team?

Speaker B: I do. They're not very good at the moment, but I'm a big football fan. Uh, I'm very curious about the upcoming World Cup. I don't have big, big hopes for the Netherlands. I saw this very analytical guy, big, big hotshot in the financial world who predicted the last two World Cups correctly, who's going to be the winner. So, uh, and now he published it again. A prediction. And he predicted the Netherlands. Um, um, but I'm, I'm not very convinced.

Speaker A: Oh, really? Wow.

Speaker B: Yeah.

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Speaker B: At least I think so. I think so. But the final is, um, far. What do you think?

Speaker A: Well, yeah, I don't know. It's so tough. I think always surprising things happen. But I think I'm correct in saying that the World cup this year has far more teams than ever before.

Speaker B: Yeah.

Speaker A: And so I think that means the group stage is easier than ever before. Because if you're a team like the Netherlands, I mean, even you can say they're not that strong, but they're still, you know, the Netherlands, I think, is, like, always one of the top 20 teams and usually, uh, one of the top 10. So when there's so many groups up to group L, what is that, 12 groups or something? Uh, yeah. It's huge then. Yeah. I mean, yeah, the group stage shouldn't be too tough, at least.

Speaker B: And that's why Italy was extra disappointing. Disappointing.

Speaker A: That's not wild. For the third time that Scotland is in and Italy isn't, for example.

Speaker B: Yeah.

Speaker A: Well, anyway, uh, I didn't mean to digress so far on sport, but some exciting weeks ahead. Uh, we've got Canada in the World cup, where I'm from, for the first time in a long time. We're definitely not going to win, but we have a few. We have a few good players. Maybe we can get out of the group. Anyway, uh, back to this subject at hand. Um, so we've been talking about kind of reprogramming, uh, childhood algorithms. Something that you talk about in your book is how behavior change often precedes changes in attitude and feelings. And I think the typical way that we think about that is the other way around. I think we would typically think, okay, if I want to change my behavior, first I have to change my attitude. But you're take. In your book, you take the opposite track and so that you. You suggest changing your behavior in order to rewire how you think and how you feel. And in your book, you talk about one particular practice that you recommend people do for 30 days to rewire their brain for higher emotional intelligence. And so do you know what example I'm talking about?

Speaker B: No, I'm not sure.

Speaker A: Um, neither do I exactly. But it's, uh, it's about putting people in uncomfortable situations. Uh, so it's an uncomfortable communication habit that a data expert can practice for 30 days to rewire their brain for higher emotional intelligence.

Speaker B: Right. Yeah. So, uh, how this works is, for example, we talked about speaking up in the train. Or also it applies to raising your hand when someone asks, like, hey, who would like to present this? I was never the person who wanted to raise their hand, but I, yeah, I made it the rule I like, okay, let's go. Let's take as many opportunities as possible. I felt uncomfortable, but I got feedback and then improved. And over time, when I did that, when I showed that behavior enough times, then also my beliefs started to change. It's changed from I'm anxious, I don't want to do this, to actually, I'm learning it's a bit more enjoyable. I would not say it's like, give me, give me all the stage, um, and I'll feel comfortable walking on it. But I enjoyed it more and I learned so much. So I rewired my brain, saying, okay, maybe this is good, this is valuable, and it's a bit enjoyable. So that's also how I felt speaking up in the train. Like in the beginning, I was like, no, I cannot do this. This is impossible. People will hate me. And then the beliefs will were like, hey, people are kind of relaxed about it.

Speaker A: Right?

Speaker B: Um, yeah, I had two conversations where they said, no, I'm listening to music. Mind your own business. I mean, it's not a fun conversation, but after two minutes, you can almost forget about it. Um, so it takes away some of the tension of the social anxiety and the friction that I really didn't want. I didn't want that friction to happen. And so much safer to stay in your comfort zone. But if you do more things that scare you, uh, then your beliefs rewire as well. You see yourself in a different way.

Speaker A: Nice. Yeah, so it's good that we had actually already talked about that specific, uh, quiet zone example, but I don't think we had made the link to how its actions change your mind downstream. And so I'm glad that we did get that point out. Anyway, 100%, you have a background in behavioral economics, and obviously you have your history as a, ah, professional poker player. And so in quite a few different ways, the data analytics, the behavioral economics the poker. All of these are kind of at an intersection of psychology and data. And now with mindspeaking, you leverage behavioral frameworks to help behavioral frameworks to help leaders navigate biases and build a culture of clear, confident decision making. Um, so you discuss things like the illusion of asymmetric insights, false consensus effect, um, as barriers to empathy and genuine influence. You've also suggested visualizing difficult colleagues as innocent children to inspire empathy. So, yeah, maybe tell us a bit more about these phenomena and if there's, like a common thread between them, maybe. But I'd also just love to hear if you have more practical, everyday techniques for analytical thinkers, um, to detect when biases are happening and how they can improve their communication abilities.

Speaker B: Yeah. A very common dynamic between data people and business people is that the business people want something fast. They're pushy, they're usually a bit more extroverted, often also more senior. And they push hard on getting a dashboard or getting a model and saying, hey, just do your magic. Just send it in an hour. Not knowing that it takes weeks to do such a thing. And it's very easy to get frustrated with them. Um, but we get frustrated, we start thinking, we start blaming them, we start thinking, ah, uh, what a jerk. Or he is totally inconsiderate. He doesn't know anything. He should take a course about data, all those things, but it's not helpful. Right? So if we become aware of those thoughts, then we can step out of that at least a little bit and start thinking. Hm, yeah, okay. It's not fun what this person is doing. But this person also has a different background. He doesn't know about data. Uh, he just tries to reach his goals by increasing revenue or decreasing cost. And if I am not so triggered by that, then everything is easier. I can do my job better, and my day is more enjoyable. And the funny thing is that many data people say, like, I'm, um, I'm very rational. I make my choices in a very rational way. But learning about behavioral science or learning about neuroscience, the opposite is true. Right? We make emotional decisions all the time. So knowing that, knowing that we get triggered in that situation, stepping outside of that, um, makes everything easier. Because, of course, you can say, like, hey, this is not how it works. It takes days. Um, looks like you're underestimating the amount of work. It's okay to push back it a little bit, but at the same time, if you're not so triggered, if you don't take it so personally, it's lighter and you're not worked up for an hour. Like, figure, oh, there's John is so annoying. Everything is so much easier. So it's good to see those biases in ourselves and those algorithms that were triggered. But this is life work, right? It's. If I'm, I'm more relaxed, I get triggered less. I see the triggers more of myself, but there's still things I'm. I'm triggered about. And I'm not very a person who gets annoyed all the time, but I do make myself difficult sometimes. So two months ago, I felt a lot of stress in my body. And then I get too analytical. I try to problem solve my way out of it. So I'm thinking, okay, maybe I can do some sports, I can do this, go to the sauna, or I see some friends. But I try to go, have the, uh, most efficient way to get myself out of the stress. But as a result, I stayed in that for days. Um, so analyzing things is very useful, but, um, not always the solution to, to everything, especially when it comes to communication.

Speaker A: Yeah. In your book, you describe the emotional brain as being like the elephant and the rational brain as the rider on top of that elephant. Um, in professional environments where logic is often rewarded over emotion, how can we respect both of those things? The emotional brain, that's kind of the undercurrent driving a lot of what we're doing, and that rational brain, that's kind of rationalizing what the elephant is doing, riding on top.

Speaker B: So the first step is always become aware of it, because we've always. Everyone had this saying, okay, after work, I'm going to do a workout, I'm going to do some sports, I'm going to eat healthily, and then one hour later, you sit on the couch watching Netflix, uh, relaxing.

Speaker A: But if you want to, if you want to get to that, uh.

Speaker B: Exactly. It's so tempting. It's so tempting for that emotional elephant, the emotional part of our brain, to, to just relax. Even though the rider on top of the elephant might say, no, no, we go that way. Take on your running shoes. So by becoming more aware of that and getting feedback, you. Yeah. You can change that behavior. Um, yeah. And that's like. Behavior is because we talk a lot about data, of course, because data is the essence of the work. But if the data doesn't help people make better decisions, then the value is zero. So the more we understand about the people and how they make decisions, the more valuable your work will become. Um, so that's also why our training focuses so much on that, how people can understand the business better, how people, um, can get buy in from stakeholders and how they can present their work in a story, taking out the technical details because it will land so much better. Um, and as you said, it helps in your personal life as well, because if you're in a relationship or with friends, we cannot analyze everything. Even though I'm tempted sometimes, for sure,

Speaker A: people are hard to understand, I think. Yeah. And then you also, I think when you, if you're quite a logical thinker, your analysis kind of might want to make a lot of assumptions about how so. And so this person at work or at home should behave rationally, and this is how it should go. But, uh, yeah, the emotional elephant could be taking them somewhere else. Anyway, so tell us a bit about now. You know, we've talked about your book. You've given us lots of tips for how analytical thinkers can speak their minds more effectively, uh, be more effective in the workplace. Let's talk now a little bit about your actual professional journey. Now, just since the pandemic, having scaled this business to, I think, uh, what did you say, 15 people or 10 people? 10 people. I'm getting the numbers mixed up because it's okay, 10 people and 15,000 professionals who have been trained by the 10 of you together, how did you scale? Uh, in a way that, you know, you give this impression. Maybe it's all the meditation or the journaling, but I get this vibe, this impression from you that, you know, you're quite relaxed, quite happy. So how do you, how have you scaled the business to this? You know, 10 people, 15,000 professionals being trained, 200,000 people reading, uh, content that your organization puts out regularly. How do you. How have you done that? And how have you done it in a way where you still seem so Zen?

Speaker B: First of all, I'm not always the Zen, Um, but I've learned a lot about this. And many, many entrepreneurs, they say, like, oh, it's so hard to build the product or to get the customers or to do the sales. For me, the biggest hurdle is the biggest challenge is actually my energy management and keeping, um, and stress management. It's not that I'm stressed all the time, but I'm making things complex. And I'm. I have this enormous drive. And also talking about algorithms, I have, I, I've had this belief for a long time that if I'm not productive, then I'm worthless. Like, if. And it was not always very extreme, but it was driving my behavior. So if I decided, okay, it's kind of the end of the day, There was always this voice in me like, you can probably do one more hour, you can probably do a few more emails, or Chris, write down this idea. I can be very hard on myself. And that's, um, that's something that is rewarding or that gets done a lot, but it's also counterproductive. You know what I said two months ago, feeling a bit stressed. Um, what I needed is rest and not feeling guilty about it. But I was still thinking about, okay, maybe tomorrow I can be have a productive day. So back to your question. Uh, how do we skill? The best decision that I've made from the beginning is writing on LinkedIn because that's how all the customers come. I don't like proactive sales and outreach so much. It doesn't energize me. I like putting out ideas in the world and then see where it resonates and then that's how clients come. So data leaders, they contact me saying, hey, I followed your work for six months, 12 months. Looks like you can help our teams. And then we have a conversation and then, yeah, more and more, uh, work comes from word of mouth. So we mainly work with big organizations. Adidas, Angie, or the big corporation, because they have so many data people. They have the tools, the smart talents, but still they're not making the impact that they want. It's not always leading to all the great work is not always leading to action or decisions. And that's when, when they bring us in. So in the beginning I didn't want to skill. I thought just, I'll be a, uh, trainer and I'll be the only person. But over time I also learned that I want to do a bit more. I like the new challenge. And also giving training every day doesn't energize me as much as I thought it would be because I'm also quite introverted. So working with a group, I love it. But at the end of the day, um, I'm pretty tired. So if I do that every day doesn't work for me. So I need more time to recharge, to get ideas, walk on the beach, meet with people one on one. And that's the, um, that's the rhythm I like.

Speaker A: On this podcast, I'm always going on about how Claude code is mind blowing. But now Claude Cowork is making my jaw drop as well. For example, I, I recently wanted to quantify how healthy my sales pipeline is for my AI consulting business. I simply asked Claude to estimate my sales for the coming quarter and it brought info from relevant Google Sheets and my Gmail to create a professional spreadsheet of clients with estimated revenue for each one. Well, this might have taken me a day. Instead, it was done flawlessly with Claude Cowork in minutes. Claude is the AI for minds that don't stop at good enough. It's the collaborator that actually understands your entire workflow and thinks with you whether you're debugging code at midnight or strategizing your next business move. Claude extends your thinking to tackle the problems that matter. Ah. Uh, and you'll appreciate that I can ask Cowork to show me data such as my sales spreadsheet. And it provides an interactive chart right in the conversation for problems worth solving. Get started with Claude at Claude AI superdata. That's Claude AI Superdata. And check out Claude Pro, which includes access to all of the features mentioned in today's episode. Claude AI Superdata as well. It certainly seems to be working for you in terms of the stats. I have your LinkedIn profile in front of me right now. It's 75,000 followers. Um, really impressive. Obviously, the content that you're creating, the thoughts that you're having when you're walking on the beach are turning into content that's resonating with people. And, uh, yeah, I mean, I totally understand your approach there for my software consulting business. Why Carrot. This podcast is one of the primary lead generators for that. And, you know, people seeing social media posts, I'm sure, about the podcast or things we're doing at the consulting firm or whatever. Yeah, it's key. Um, so that definitely makes a lot of sense for people, uh, who want to follow your work. After this episode, beyond your LinkedIn profile, where. What are you. Where places that people should follow you.

Speaker B: Yeah. So beyond LinkedIn, you can subscribe to the YouTube channel. It's still very fairly new, but if you're able to type my name, then you'll find me. So it's. It's Gilbert, Gilbert eichelin home Gilbert EI. And then you'll probably find me or find MindSpeaking, name of the company. So that's the main thing. Also have a newsletter and some other free materials on mindspeaking.com our website. And yeah, we'd love to, uh, hear from People, connect on LinkedIn, send me an email or reach out via the website. There's a lot of, um, valuable stuff there for free.

Speaker A: Yeah, we'll have all of those links for you in the show notes, of course. So whether you can spell Gilbert's last name. Eichelinbaum uh, there's my attempt. Uh, whether you can. Whether you can spell that or not, you'll be able to find him. Also, mind speaking, one word is very easy to spell. So that's nice.

Speaker B: Yeah, true. That's the. That's outwaldes.

Speaker A: Nice. And so usually I ask that kind of social media following question last, but I just kind of felt like I had the flow right into it there from the preceding question. So my final question for you today, Gilbert, is, do you have a book recommendation for us other than your own book?

Speaker B: The book I really like is a book by Matthew Dix, Story Worthy. It's not about data. Uh, it's not about technical skills, but it's about storytelling. And not just about business, but also how you tell stories in daily life and why it matters and how you make them more concise. I've learned that storytelling is super powerful. It's really fun to learn about it, and it makes your work so much more impactful.

Speaker A: What was the name of the book again? Story writing.

Speaker B: Story Wordy.

Speaker A: Oh, Story Worthy.

Speaker B: Worthy.

Speaker A: I gotcha. I found it. Yeah.

Speaker B: Yeah.

Speaker A: Uh, I was. I was typing in Story Wordy with a D like nothing was coming up. Yeah, Story Worthy. Wow. Yeah, it's got a lot of reviews. It's a really popular book.

Speaker B: Yeah, it's a really good book. And the other one I will shout out is the, um, Is the book by Daniel Kahneman. It's very famous, but, uh, Thinking fast and Slow.

Speaker A: Thinking Fast and slow.

Speaker B: Exactly. It will teach you a lot about how we think, why we behave like we behave, and, yeah, I cannot emphasize those skills enough for data people if you want to make an impact. Yeah.

Speaker A: Listeners, you can check out superdatascience.com books for any of the books that have ever been recommended on the thousand plus episodes of this podcast. And Daniel Kahneman's Thinking Fast and Slow is one of the most recommended books ever on the podcast.

Speaker B: Cool.

Speaker A: Yeah. All right, thanks for those recommendations, Gilbert. And so, uh, generous of you to take time out of your busy schedule and talk to me and talk to our listeners here. Really appreciate you doing it, and hopefully we can get you on again at some point in the future. And actually, you may not even know this, but, uh, it was Kate Strachny that recommended, uh, you as a guest, and that's what immediately precipitated me reaching out to you. I'd been aware of you for a

Speaker B: while, but that's cool.

Speaker A: Yeah. For saying that led me to ask you to be on the show.

Speaker B: Oh, great. Thanks Kate, for the recommendation leading to this conversation. I really enjoyed it, John, so thanks a lot for having me for this conversation.

Speaker A: Super episode today. In it, Hilbert Hlenbaum, if I am even getting somewhere close to pronouncing his name correctly, detailed why no matter matter how good your model or analysis is, it only creates value once people actually use it, which makes communication a core data skill rather than an optional extra. He provided us with the and but therefore framework, where instead of stacking detail on detail, you set up a situation, introduce a twist, and land on a clear recommendation, just like every good movie. He talked about how research suggests only about 50 15% of people are quote unquote self aware, meaning their view of themselves genuinely matches how others see them and why things like journaling, meditation and exercise, being aware of your body help close that gap. He talked about how experiences in childhood install personal algorithms in our adult behavior, like avoiding conflict or staying silent, and how asking people what you should keep, stop and start doing helps surface these algorithms for us and, uh, helps us be more aware of them if there is something you want to change about yourself. He talked about how behavior change comes before attitude change, so doing small, uncomfortable things for a time period like 30 days can actually rewire your beliefs about yourself. All right, as always, you can get all the show notes, including the transcript for this episode, the video recording, any materials mentioned on the show, the the URLs for Hilbert's social media profiles, as well as my own@superdatascience.com 1005. Thanks to everyone on the Super Data Science podcast team. Our podcast manager Sonja Brayevich, media editor Mario Pombo, partnerships manager Natalie Zhajsky, researcher Serge Macis, and our founder Kirill Aramenko. Thanks to all of them for producing another sensational episode for us today, for enabling that gray team to create this free podcast for you. We are deeply grateful to our sponsors. Uh, we couldn't do this without them. Um, and we couldn't do this without you either. So yeah, feel free to go ahead and support our show by checking out our sponsors links which are in the show notes. And if you ever want to sponsor an episode yourself, you can get the details on how to do that@johnkrohn.com podcast. Otherwise, please do help us out by sharing this episode with the people who would also like to improve their people skills. Review this show on your favorite podcasting app or on YouTube subscribe. But most importantly, just keep on tuning in. I'm so grateful to have you listening and I hope I can continue to make episodes you love for years and years to come. Till next time, keep on rocking it out there, and I'm looking forward to enjoying another round of the Super Data Science Podcast with you very soon.